A Digitally Testable $\Sigma-\Delta$ Modulator Using the Decorrelating Design-for-Digital-Testability
Bibliographic record
Abstract
This paper demonstrates a digitally testable second-order Σ - Δ modulator. The modulator under test (MUT) employs the decorrelating design-for-digital-testability (D3T) scheme to provide two operation modes: the normal mode and the digital test mode. In the digital test mode, the input switched-capacitor network of the D3T modulator is reconfigured as two sub-digital-to-charge converters (sub-DCCs). Each of the sub-DCCs accepts a Σ - Δ modulated bit-stream as its test stimulus. By repetitively inputting the DCCs with the same Σ - Δ modulated bit-stream but with different delays, the DCCs incorporates with the integrator to generate the analog stimulus in the digital test mode. The analog stimulus is analogous to the result of filtering the bit-stream with a two-nonzero-term FIR decorrelating term. Consequently, the D3T MUT suffers less from the undesired shaped noise of the digital stimuli, and achieves better digital test accuracy. Measurement results show that the digital tests present a peak signal-to-noise-and-distortion ratio (SNDR) of 80.1 dB at an oversampling ratio of 128. The SNDR results of the digital tests differ from their conventional analog counterparts by no more than 2 dB except for the -3.2 dBFS test. The analog hardware overhead of the D3T MUT only consists of 13 switches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".